Motivated by the advances of deep learning and Convolutional Neural Networks (CNNs) for scene understanding, there have been many semantic SLAM techniques exploiting this information using cameras [1]}, [2]}, cameras + IMU data [3]}, stereo cameras [4]}, [5]}, [6]}, [7]}, [8]}, or RGB-D sensors [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]}. Most of these approaches were only applied indoors and use either an object detector or a semantic segmentation of the camera image.
In contrast, we only use laser range data and exploit information from a semantic segmentation operating on depth images generated from LiDAR scans.
